# -*- coding: utf-8 -*-

# use single neural network
#
import os

import loaders.ocr_loader as ml
import tensorflow as tf

mnist = ml.get_mnist_set(True)

sess = tf.InteractiveSession()
x = tf.placeholder("float", shape=[None, 56*28])
y_ = tf.placeholder("float", shape=[None, 26])

W = tf.Variable(tf.zeros([56*28,26]))
b = tf.Variable(tf.zeros([26]))

sess.run(tf.global_variables_initializer())

saver = tf.train.Saver(tf.global_variables())

# The default is -1 which indicates the last dimension
# softmax = exp(logits) / reduce_sum(exp(logits), dim)
y = tf.nn.softmax(tf.matmul(x,W) + b)

cross_entropy = -tf.reduce_sum(y_*tf.log(y))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)


for i in range(1000):
    batch = mnist.letter.next_batch(10)
    train_step.run(feed_dict={x: batch[0], y_: batch[1]})

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))


model_dir = '/Users/vista/PycharmProjects/data/model/ocr_1/'
saver.save(sess, model_dir + 'm')
# if os.path.exists(model_dir + 'm.index'):
#     saver.restore(sess, model_dir + 'm')

def save_sess(sess0):
    sess0.run(W).tofile('/Users/vista/CLionProjects/opencv-ocr/model/w1.dat')
    sess0.run(b).tofile('/Users/vista/CLionProjects/opencv-ocr/model/b1.dat')

save_sess(sess)

print accuracy.eval(feed_dict={x: mnist.letter.images, y_: mnist.letter.labels})